Personalization Approaches.
There have been a few different approaches that proposed learning schemes through meta-learning, local fine-tuning, multi-task learning, model regularization, contextualization, and model interpolation to build personalized models.
For instance, Per-FedAvg uses Meta-Learning [1]}, [2]} to learn a common initialization point for all the users, which is then adapted to each user with a couple of steps of gradient descent.
Another approach, pFedMe [3]}, re-formulates the FL objective as a bi-level optimization problem and modifies the minimized loss function with the inclusion of a regularization term.
Lastly, FedPer [4]} splits a deep neural network into base and personalization layers, where the base layers are learned collaboratively, and personalization layers are specific to each user.
However, some of these approaches incur high computational and algorithmic complexity. For instance, pFedMe and Per-FedAvg require a higher number of global communication rounds compared to FedPer.
Model Agnostic Meta-Learning based methods (MAML) [5]} (used in Per-FedAvg) require the computation of the Hessian matrix, which significantly adds computational complexity to each client.
